Unsupervised feature selection based on spectral regression from manifold learning for facial expression recognition

In this study, an unsupervised feature selection method is proposed for facial feature recognition (FER) in the absence of class labels. The contribution is the descriptive feature components selector spectral regression representative coefficient scores based on graph manifold learning from high-dimensional feature space. The spectral regression analysis and L1-regularised least square are then used to compute the importance of features in the original space, so that less representative features with lower coefficient scores will be removed without prior distribution assumption. To verify the performance of the authors' method, some classifiers are used to classify facial expressions on three benchmark facial expression databases. The recognition results indicate the availability and effectiveness of the proposed method for FER.